Adversarial imitation learning-based network for category-level 6D object pose estimation

Category-level 6D object pose estimation is a very fundamental and key research in computer vision. In order to get rid of the dependence on the object 3D models, analysis-by-synthesis object pose estimation methods have recently been widely studied. While these methods have certain improvements in generalization, the accuracy of category-level object pose estimation still needs to be improved. In this paper, we propose a category-level 6D object pose estimation network based on adversarial imitation learning, named AIL-Net. AIL-Net adopts the state-action distribution matching criterion and is able to perform expert actions that have not appeared in the dataset. This prevents the object pose estimation from falling into a bad state. We further design a framework for estimating object pose through generative adversarial imitation learning. This method is able to distinguish between expert policy and imitation policy in AIL-Net. Experimental results show that our approach achieves competitive category-level object pose estimation performance on REAL275 dataset and Cars dataset.